Typically, system designers limit the size of a crossbar to reduce energy consumption Energy consumption in a crossbar scales proportional to \(M^2\) , where \(M\)  is the input/output dimension of a crossbar. and mitigate the high parasitic voltage drops within a crossbar (see Figure REF ). Therefore, a large machine learning model must be partitioned into local synapses, those that map within the crossbar of a tile, and global synapses, those that map on the shared interconnect [1]}. To effectively address this partitioning, NeuroXplorer's system software performs the following four key functionalities to map a machine learning workload to the hardware: workload decomposition, model clustering, cluster mapping, and runtime.
We now describe these functions.
